Library Guides: AI (Artificial Intelligence) Knowledge, Tools, and Resources: AI Concepts/Terminology

As this guide is about AI and Generative AI, four Generative AI chatbots are being used in creating portions of the content provided. The four bots being used are ChatGPT 3.5 (CG), New Bing (NB), Bard (B), and Claude.ai (C).

Many terms and concepts get bundled into the idea of artificial intelligence. It is important to distinguish these from the general AI concept.

Prompt - What are concepts related to artificial intelligence?
Primary response from ChatGPT 3.5
Also included in responses from Bing (b1), Bard (b2), Claude.ai (c)

Artificial Intelligence (AI) is a broad and multidisciplinary field with many related concepts and subfields. Here are some of the key concepts and areas related to AI:

Machne Learning (b1, b2, c)
A subset of AI that focuses on developing algorithms and models that enable computers to learn from and make predictions or decisions based on data. It includes supervised learning, unsupervised learning, and reinforcement learning.
Deep Learning (b1,b2, c):
A subfield of machine learning that involves neural networks with many layers (deep neural networks). Deep learning has been highly successful in tasks like image and speech recognition.
Natural Language Processing (NLP) (b1 b2, c):
NLP is the study of how computers can understand, interpret, and generate human language. It's essential for chatbots, language translation, sentiment analysis, and more.
Computer Vision (b1, b2) :
This field focuses on enabling computers to interpret and understand visual information from the world, such as images and videos. It's used in facial recognition, object detection, and autonomous vehicles.
Robotics (b1 b2, c):
The study of creating autonomous or semi-autonomous machines that can perform tasks in the physical world. Robotics incorporates elements of AI, sensors, and control systems.
Expert Systems (b1, c) :
AI systems designed to mimic the decision-making abilities of a human expert in a specific domain. They use knowledge representation and inference to make informed decisions.
Reinforcement Learning:
A type of machine learning where an agent interacts with an environment and learns to take actions that maximize a reward. It's used in applications like game playing and robotics.
Artificial Neural Networks: (b1, c)
Computational models inspired by the structure and function of the human brain. These networks are fundamental in deep learning and pattern recognition.
Cognitive Computing:
AI systems that aim to mimic human cognitive functions like problem-solving and decision-making. They often leverage natural language processing and machine learning.
AI Ethics:
The study of ethical issues and considerations related to AI, including bias, transparency, accountability, and the impact of AI on society.
Machine Vision:
The use of computer vision techniques to automate inspection and analysis tasks in industrial settings.
Autonomous Systems:
Systems that can operate independently and make decisions without human intervention. Examples include self-driving cars and drones.

These are just a few of the many concepts and subfields related to artificial intelligence. AI is a rapidly evolving field, and new ideas and subfields continue to emerge as researchers and practitioners explore its potential applications and challenges.

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Speech recognition:
Technology that can identify and understand spoken language and convert it to text. Enables applications like voice assistants and voice search.
Planning and optimization:
Using algorithms to determine the best course of action to achieve a goal given constraints and limited resources. Applied in self-driving vehicles, logistics, etc.

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Algorithms:
Algorithms are step-by-step instructions that computers can follow to solve problems. AI algorithms are designed to allow computers to learn and adapt, and they are used in a wide range of AI applications.
Data:
Data is the fuel that powers AI. AI models are trained on data, and they use that data to learn patterns and make predictions. The quality and quantity of data are essential for the success of AI systems..

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Items unique from Bing

Artificial General Intelligence:
Artificial general intelligence (AGI) is a hypothetical type of intelligent agent that could learn to accomplish any intellectual task that a human being or animal can perform. It is also referred to as strong AI or universal AI.
Artificial superintelligence (ASI):
Refers to the hypothetical ability of machines to surpass human intelligence in all domains.

Generative AI has become a highly publicized type of artificial intelligence since the introduction of ChatGPT. As with the term artificial intelligence, there are many concepts related to generative AI.

Large Language Models -LLMs
Interestingly, when using the prompt below, none of the 4 AI bots being used mentioned Large Language Models (LLMS) that are the foundation of these systems.

Prompt - What are concepts related to generative AI?
Primary response from ChatGPT 3.5
Also included in responses from Bing (b1), Bard (b2), Claude.ai (c)

Generative AI is a branch of artificial intelligence that focuses on creating models and systems capable of generating new content, often in the form of text, images, audio, or other data types. Here are some key concepts related to generative AI:

Generative Models: These are the core algorithms used in generative AI. They learn patterns and distributions in data and can generate new samples that resemble the training data. Popular generative models include:

Generative Adversarial Networks (GANs):
GANs consist of a generator and a discriminator, which are trained in a competitive manner. The generator tries to produce data that is indistinguishable from real data, while the discriminator tries to tell real from fake. This adversarial training results in high-quality generated content.
Variational Autoencoders (VAEs):
VAEs are a type of probabilistic generative model that focuses on learning a probabilistic mapping from data to a latent space and back. They are often used for data generation and compression.
Transformers:
Transformers are a versatile architecture that can be used for various generative tasks. They've been particularly successful in natural language processing tasks, such as language translation and text generation.
Natural Language Generation (NLG):
NLG is a specific application of generative AI that focuses on creating human-like text. It's used in chatbots, content generation, and other language-related tasks.
Data Augmentation:
Generative AI can be used to augment datasets for training machine learning models. For example, it can generate additional data to improve the performance of classifiers or other AI systems.
Conditional Generation:
Some generative models can generate content based on specific conditions or input. For example, you can condition a text generator to produce content in the style of a particular author.

These concepts form the foundation of generative AI and its applications in various domains, from creative arts to data science and natural language understanding.

AI (Artificial Intelligence) Knowledge, Tools, and Resources

AI Concepts/Terminology

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